{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Plotting with Numpy and Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x70ba4a8>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kq+ZM5TRxJmjmXM5UThNnKqvM1sda4N8y8z8z82ngr4G3L3bh3r3wxjfCDTf0KvojH6n3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\nf8y56VCHfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6fa6908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(10), np.arange(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x78bb828>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A+/R5/a1v33xtHbW3tGQ16v79D3477LDXPz7mmI77dPb5yhF2c3N2M2t0PXog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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x71def98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(10), np.exp(np.arange(10)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7c7f3c8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IWC3pNmA1sAO4LOfKvxy4AdgfWNJXADArlJuCzIrnlcWs4d18MyxZArfckt/+rglYM/LK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\nMDNrYQ4CZmYtzEHAzKyFOQiYmbWw/w8bH2CLAQxaUQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x78d72b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.subplot(121)\n",
    "plt.plot(np.arange(10), np.exp(np.arange(10)))\n",
    "plt.subplot(122)\n",
    "plt.scatter(np.arange(10), np.exp(np.arange(10)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fa5128>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VezhoEvBERKyJiM3AjaRexW2jngcSHySb8/16enqYOvV8Nm5czPr1D7Bx42Km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\nVAg0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7e184a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.subplot(211)\n",
    "plt.plot(np.arange(10), np.exp(np.arange(10)))\n",
    "plt.subplot(212)\n",
    "plt.scatter(np.arange(10), np.exp(np.arange(10)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x85954a8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lj/Tekxr3THj9Slx2ICGlW7eaJ5KXgalQKDBkSDswLi4Zx+DBbRQKhcT7UFxL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\nIk3s/wNnwYNLqRi4ogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x816c7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.subplot(221)\n",
    "plt.plot(np.arange(10), np.exp(np.arange(10)))\n",
    "plt.subplot(222)\n",
    "plt.scatter(np.arange(10), np.exp(np.arange(10)))\n",
    "plt.subplot(223)\n",
    "plt.scatter(np.arange(10), np.exp(np.arange(10)))\n",
    "plt.subplot(224)\n",
    "plt.scatter(np.arange(10), np.exp(np.arange(10)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x94d24e0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9EhOPOdY/585tGdvQ0CBTJ0yQWLtd+oaESL+kJHE4HC3bV61aJZEhIZIWGirh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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x94827b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "\n",
    "iris = load_iris()\n",
    "data = iris.data\n",
    "target = iris.target\n",
    "\n",
    "# Resize the figure for better viewing\n",
    "plt.figure(figsize=(12,5))\n",
    "\n",
    "# First subplot\n",
    "plt.subplot(121)\n",
    "\n",
    "# Visualize the first two columns of data:\n",
    "plt.scatter(data[:,0], data[:,1], c=target)\n",
    "\n",
    "# Second subplot\n",
    "plt.subplot(122)\n",
    "\n",
    "# Visualize the last two columns of data:\n",
    "plt.scatter(data[:,2], data[:,3], c=target)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
